Systems and methods for determining likelihood of incident occurrence

ABSTRACT

Determination of a user-specific likelihood of incident occurrence at a geographic location may be performed. User information, historical incident information, contextual information, and/or other information may be obtained. User information may include user demographic information, user behavior information, user social information, and/or other user related information. Historical incident information may include data relating to crime, mortality, injury, morbidity rates and may be obtained from local law enforcement, local Departments of Motor Vehicles, national security agency, foreign security agency, international criminal policy organization, national public health agency, international public health agency and/or other sources. Contextual information may include information about events that have previously occurred at or near user&#39;s current geographic location. Determination of a user-specific likelihood of incident occurrence may be performed by analyzing collected sets of user data, historical incident data, and contextual information obtained from various sources to create a single incidence likelihood indicator.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/676,766, filed on Aug. 14, 2017, which is a continuation of Ser. No.15/141,670, filed on Apr. 28, 2016, now U.S. Pat. No. 9,734,456, thecontents of which are incorporated by reference herein.

FIELD

The disclosure relates to systems and methods for determining auser-specific likelihood of incident occurrence at a geographiclocation.

BACKGROUND

Many mobile devices now have the capability of recording location andother information. Having a complete record of when and where the usergoes is useful for a variety of applications, including incidentoccurrence alerts, recommendation systems, lifelogging, gaming, and goaltracking. Multiple data sources exist for storing and tracking criminal,health, safety, and other relevant information associated with certaingeographic locations and certain demographics. The information may oftenbe of widely different types and scattered across various physicalsystems belonging to different organizations and/or individuals.

SUMMARY

The disclosure relates to determining a likelihood of incidentoccurrence for the geolocation, the time, and the user and presenting itto users on client computing devices, in accordance with one or moreimplementations. The determination may be performed by analyzinggeolocation, user information, historical incident information, andcontextual information. The geolocation information may be obtained fromthe user client computing device. User information, including userdemographic characteristics, user behavioral information, user socialinformation, and/or other information may be obtained as user input orobtained from various sources. Historical incident information may beobtained from various sources and reflect information for incidents thathave previously occurred at or near user geographic location includingdemographic information. Contextual information may include informationabout events that are occurring or have previously occurred at or nearuser geographic location and may be obtained from various sources.

In some implementations, system configured to determine a likelihood ofincident occurrence for the geolocation and the user may include one ormore servers. The server(s) may be configured to communicate with one ormore client computing device according to a client/server architecture.The users of the system may access the system via client computingdevices. The server(s) may be configured to execute one or more computerprogram components. The computer program components may include one ormore of a geolocation component, a user component, an incidentcomponent, a contextual component, a determination component, apresentation component, and/or other components.

The geolocation component may be configured to obtain geolocationinformation for the likelihood of incident occurrence. Geolocationinformation may include determination of a real-world position orgeographic location of a user. The geolocation component may beconfigured to use client computing device to determine a geographicallocation of a user based on one or more of signal strength, GPS, celltower triangulation, Wi-Fi location, receipt of real-world location fromthe server, and/or other input. In some implementations, user movementsmay be tracked using a geography based transmitter on the clientcomputing device. Future location may be a location the user isintending on visiting sometime at a later time on the same date. Futurelocation may be a location the user is intending on visiting sometime ata later date. In some implementations, the geolocation component may beconfigured to obtain user's speed and/or direction of traveling. Futurelocation may be obtained using user's speed and/or direction oftraveling.

The user component may be configured to obtain user demographiccharacteristics. The user demographic characteristics may include user'sage, sex, race, national original, religion, marital status, familystatus, sexual orientation, height, weight, occupation, career,education level, interests, hobbies and/or other characteristics. Theuser component may be configured to obtain user demographiccharacteristics as stated information from the user, from anadministrator, obtained from analysis of publicly available informationassociated with the user (e.g., public records, social media, and/orother sources), determined based on behavioral and/or demographicinformation related to the user, and/or other sources of demographiccharacteristics.

The user component may be configured to obtain user behavioralinformation at a geographic location. User behavioral information mayinclude information about user activities both online and offline. Forexample, user's online behavior may include sites visited, appsdownloaded, or games played. User's offline behavior may include venuesvisited, mode of transportation used, speed of traveling, among other.User behavioral information may be determined based on input obtainedfrom user's schedule, by inference from location (for example, a user'slocation may indicate a user is in or near a stadium) and/or user'scalendar events.

The user component may be configured to obtain user social information.Social information may include information related to user's profilestored in connection with user's account with a social networkingsystem. User profile may include information provided by the user andinformation gathered by various systems, including the social networkingsystem, relating to activities or actions of the user.

The incident component may be configured to obtain historical incidentinformation for incidents that have previously occurred at or near usergeographic location. Historical incident information may includeinformation about incidents that have previously occurred at or nearuser geographic location including frequency of occurrences, duration,outcome, type of users involved, time(s) of occurrence, and/or otherspecific information characterizing historical incidents. The incidentcomponent may be configured to obtain historical incident informationfrom a variety of sources. The Incident component may be configured toobtain historical incident information as stated information from theuser, from an administrator, obtained from analysis of publiclyavailable information associated with the geographic location (e.g.,public records, social media, and/or other sources), and/or othersources of historical incident information.

The contextual component may be configured to obtain contextualinformation describing events occurring at or near user geographiclocation. Contextual information may include information about eventsthat have previously occurred at or near user geographic location.Contextual information may include information about events that occuron an individual basis. Contextual information may include informationabout events that occur periodically, regularly, and/or sporadically.The contextual component may be configured to obtain contextualinformation as stated information from the user, from an administrator,obtained from analysis of publicly available information associated withthe geographic location (e.g., public records, social media, and/orother sources), determined based on behavioral and/or demographicinformation related to the user, and/or other sources of contextualinformation.

The determination component may be configured to determine a likelihoodof incident occurrence for a user at a certain geographic location byanalyzing information obtained by the geolocation component, the usercomponent, the incident component, and the contextual component.

The determination analysis performed by the determination component maybe configured to utilize a variety of analytical techniques to analyzecollected sets of user data, historical incident information, andcontextual information obtained from various sources to create a singleincidence likelihood indicator. The determination component may beconfigured to determine a likelihood of incident occurrence usingBayesian-type statistical analysis to calculate the incidence likelihoodindicator. The determination component may be configured to assignspecificity, relevance, confidence and/or weight to every one ofgeographic information, user information, historical incidentinformation, and contextual information based on the relevance andrelationship between each piece of information to one another. Theassignment of these weight factors may be used in determination ofuser-specific likelihood results.

The determination component may be configured to determinelocation-based likelihood indicators associated with the individualusers based upon user information, historical incident data, andrelevant contextual information. The likelihood indicators may quantifya likelihood of negative incident occurrence to individual users at acertain geographic location. In some implementations, the likelihoodindicators may quantify a likelihood of occurrence of certain types ofnegative incidents to individual users at a certain geographic location.

The presentation component p may be configured to effectuatepresentation of an incidence likelihood indicator to the user, theincidence likelihood indicator reflecting the likelihood of incidentoccurrence for the geolocation and the user. The presentation componentp may be configured to use the client computing device(s) to present theincidence likelihood indicator to the user.

These and other objects, features, and characteristics of the systemand/or method disclosed herein, as well as the methods of operation andfunctions of the related elements of structure and the combination ofparts and economies of manufacture, will become more apparent uponconsideration of the following description and the appended claims withreference to the accompanying drawings, all of which form a part of thisspecification, wherein like reference numerals designate correspondingparts in the various figures. It is to be expressly understood, however,that the drawings are for the purpose of illustration and descriptiononly and are not intended as a definition of the limits of theinvention. As used in the specification and in the claims, the singularform of “a”, “an”, and “the” include plural referents unless the contextclearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system configured for determining a likelihood ofincident occurrence for a user at a geographic location, in accordancewith one or more implementations.

FIG. 2 illustrates an exemplary determination analysis utilizinggeographic location, user demographic information, user behavioralinformation, user social information, historical incident information,contextual information, in accordance with one or more implementations.

FIG. 3 illustrates an exemplary schematic of an implementation of thesystem of FIG. 1, in accordance with one or more implementations.

FIG. 4 illustrates a method for determining a likelihood of incidentoccurrence for a user at a geographic location, in accordance with oneor more implementations.

DETAILED DESCRIPTION

FIG. 1 illustrates a system configured for determining a likelihood ofincident occurrence for the geolocation and the user and presenting itto users on client computing devices, in accordance with one or moreimplementations. As is illustrated in FIG. 1, system 100 may include oneor more servers 102. Server(s) 102 may be configured to communicate withone or more client computing device 104 according to a client/serverarchitecture. The users of system 100 may access system 100 via clientcomputing devices(s) 104. Server(s) 102 may be configured to execute oneor more computer program components. The computer program components mayinclude one or more of geolocation component 106, user component 108,incident component 110, contextual component 112, determinationcomponent 114, presentation component 116 and/or other components.

Geolocation component 106 may be configured to obtain geolocationinformation for the likelihood of incident occurrence. Geolocationinformation may include determination of a real-world position orgeographic location of a user. Geolocation component 106 may beconfigured to use client computing device 104 to determine ageographical location of a user based on one or more of signal strength,GPS, cell tower triangulation, Wi-Fi location, receipt of real-worldlocation from server 106, and/or other input. In some implementations,user movements may be tracked using a geography based transmitter onclient computing device 104. For example, a user may have arrived to NewYork City by landing at the John F. Kennedy airport. User's firstlocation may be obtained as a 3-mile radius area surrounding theairport. Next, user may take a taxi cab to user's hotel in midtownManhattan. User's second location may be obtained using client computingdevice 104 as a 3-mile radius area surrounding New York Hilton HotelMidtown. In some implementations, geolocation component 106 may receiveuser input referring to a future user location. Future location may be alocation user is intending on visiting sometime at a later time on thesame date. For example, user staying in a hotel in midtown Manhattan maybe attending a concert later that night. Geolocation component 106 maybe configured to obtain user location as the area surrounding theconcert venue as provided by the user. Future location may be a locationuser is intending on visiting sometime at a later date. For example,user may be traveling to a medical device convention to Cleveland, Ohioduring last week of May. Geolocation component 106 may be configured toobtain user location as the convention center in Cleveland, Ohio asentered by the user. In some implementations, geolocation component 106may be configured to obtain user's speed and/or direction of traveling.For example, user may be in in a motor vehicle using an interstate andmoving at a particular speed which may be obtained by geolocationcomponent 106. Future location may be obtained using user's speed and/ordirection of traveling. For example, user may be in in a motor vehicletraveling north on interstate 5 from Irvine, Calif. at a speed of 80mph. Geolocation component 106 may be configured to obtain future userlocation as Santa Ana, Calif. as it is the next city on user's way.

User component 108 may be configured to obtain user demographiccharacteristics. User demographic characteristics may include user'sage, sex, race, national original, religion, marital status, familystatus, sexual orientation, height, weight, occupation, career,education level, interests, hobbies and/or other characteristics. Usercomponent 108 may be configured to obtain user demographiccharacteristics as stated information from the user, from anadministrator, obtained from analysis of publicly available informationassociated with the user (e.g., public records, social media, and/orother sources), determined based on behavioral and/or demographicinformation related to the user, and/or other sources of demographiccharacteristics. For example, the user may enter user demographiccharacteristics directly from the user via user's input into clientcomputing device 104. In some implementations, user component 108 may beconfigured to obtain user demographic characteristics from online publiccontent. An online public content may include one or more of an onlinevideo content, a social media content, an online photo content, audiocontent, and/or other online public content. An online platform mayinclude a networking platform a media platform, and/or other onlineplatforms. The online platform may include the online public contentand/or make the online public content available for consumption. Forexample, an online platform may include YOUTUBE, FACEBOOK, TWITTER,PINTEREST, LINKEDIN, FOURSQUARE, GOOGLE+, FLICKR, TUMBLR, BLOGGER, VINE,INSTAGRAM, SNAPCHAT, MAKER.TV and/or other online platforms. Forexample, user may input into system 100 their name, age, and race. Usingthe online public content, user component 108 may obtain data thatspecific user, based on photo content, prefers a style of dressassociated with a known athletic team/has extravagantaccessories/flashy/wears hoodies, etc.

In some implementations, user component 108 may be configured to obtainuser supplemental information such as user medical record, includingmedical history and medication use, user driving record, user arrestrecord, user vaccination record, user gun ownership record, userfinancial record, and/or other supplemental information. User component108 may be configured to obtain user supplemental information directlyfrom the user via user's input into client computing device 104.

User component 108 may be configured to obtain user behavioralinformation at a geographic location. User behavioral information mayinclude information about user activities both online and offline. Forexample, user's online behavior may include sites visited, appsdownloaded, or games played. User's offline behavior may include venuesvisited, mode of transportation used, speed of traveling, among other.User behavioral information may be determined based on input obtainedfrom user's schedule and/or user's calendar events. For example, user'scalendar may include entries related to user attending a weekly companysponsored softball practice event. User behavioral information mayinclude attendance of a weekly softball practice during the relevanttime period.

User component 108 may be configured to obtain user social information.Social information may include information related to user's profilestored in connection with user's account with a social networkingsystem. User profile may include information provided by the user andinformation gathered by various systems, including the social networkingsystem, relating to activities or actions of the user. For example, theuser may provide his name, profile picture, contact information, birthdate, gender, marital status, family status, employment, educationbackground, preferences, interests, and other demographical informationto be included in his user profile. The user may identify other users ofthe social networking system that the user considers to be his friends.A list of the user's friends or first degree contacts may be included inthe user's profile. Connections in social networking systems may be inboth directions or may be in just one direction. In someimplementations, a social networking system allow the connection to beindirect via one or more levels of connections (e.g., friends offriends). Connections may be added explicitly by a user, for example,the user selecting a particular other user to be a friend, orautomatically created by the social networking system based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). The user may identify or bookmark websites orweb pages he visits frequently and these websites or web pages may beincluded in the user's profile. The user may provide informationrelating to various aspects of the user (such as contact information andinterests) at the time the user registers for an account or at a latertime. The user may also update his or her profile information at anytime. For example, when the user moves, or changes a phone number, hemay update his contact information. Additionally, the user's interestsmay change as time passes, and the user may update his interests in hisprofile from time to time. A user's activities on the social networkingsystem, such as frequency of accessing particular information on thesystem, may also provide information that may be included in the user'sprofile. Again, such information may be updated from time to time toreflect the user's most-recent activities. Still further, other users orso-called friends or contacts of the user may also perform activitiesthat affect or cause updates to a user's profile. For example, a contactmay add the user as a friend (or remove the user as a friend). A contactmay also write messages to the user's profile pages—typically known aswall-posts. A user may also input status messages that get posted to theuser's profile page. User's profile may contain information about user'sevents that are attended by the user or user's contacts.

User component 108 may be configured to obtain user's biometricinformation for the geolocation. Biometric data may be data describing ameasurement of vital statistics of the user over a given period of time,a set of physiological responses of the user, and/or other data. Thevital statistics of the user may include a heart rate, respiratory rate,and/or blood pressure. The set of physiological responses may include,without limitation, heart rate, pupil dilation, respiration, bloodpressure, body temperature, rate of perspiration, and/or voice stressfor the voice of the user. Biometric data may be received from a set ofbiometric sensors associated with client computing platform 104. Forexample, a health and fitness tracking devices equipped with biometricsensors may be used to provide biometric data. In anotherimplementation, user component 108 may be configured to obtain a changein a biometric reading associated with the user is compared with athreshold or baseline reading of the same user.

User component 108 may be configured to obtain information about otherusers and/or animals that may be present with the user at a certaingeographic location at a time of determining likelihood of incidentoccurrence. For example, user may be at a certain geographic locationalone or accompanied by user's spouse, parents, children, or friends. Insome implementations, the information regarding user's family membersmay be gathered using user demographic, user behavioral, user socialand/or other information. For example, user may be in a theme park withhis 2-year-old. System 100 may determine/obtain information includingage, sex, and hobbies of the child using user social information, userbehavioral information, and/or other information. A user may beaccompanied by a friend that has written anti-governmental posts onuser's FACEBOOK and have a hobby of visiting gun ranges. User may notshare the same conspiracy theories as his friend, the informationobtained about user's friend with may be used by System 100.

User component 108 may be configured to obtain information associatedwith individual users. Information may be stored by server(s) 102,client computing platforms 104, and/or other storage locations.

Incident component 110 may be configured to obtain historical incidentinformation for incidents that have previously occurred at or near usergeographic location. Historical incident information may includeinformation about incidents that have previously occurred at or nearuser geographic location including frequency of occurrences, duration,outcome, demographic information of those involved, time(s) ofoccurrence, and/or other specific information characterizing historicalincidents. Incident component 110 may be configured to obtain historicalincident information from a variety of sources. Incident component 110may be configured to obtain historical incident information as statedinformation from the user, from an administrator, obtained from analysisof publicly available information associated with the geographiclocation (e.g., public records, social media, and/or other sources),and/or other sources of historical incident information. For example,historical incident information may include information relating to oneor more of crime, mortality, injury, morbidity rates, and/or otherphenomena. Historical incident information may be obtained from one ormore of local law enforcement agency, local Departments of MotorVehicles, national security agency such as the Federal Bureau ofInvestigation, intelligence agency such as the Central IntelligenceAgency, international criminal policy organization such as Interpol,national public health agency such as Center for Disease Control,international public health agency such as World Health Organization,subject matter expert, and/or other sources. For example, a generalcriminal activity rate for a geographic location may represent allcriminal acts that have occurred in that location. A criminal activityrate may include violent and non-violent crimes, crimes against theperson and/or property, and so on. A general mortality rate may includedeath from murder, morbidity rate, and death from natural causes. Insome implementations, incident component 110 may be configured toprioritize or rate the obtained historical incident information based onindividual user information. For example, morbidity rate at a geographiclocation including a hospital may be more relevant for a user over 65years old currently being hospitalized than a healthy 35-year-old thatexercises regularly. In some implementations, historical incidentinformation may include data relating to crime, mortality, injury,morbidity rates and may be obtained from proprietary data sets.

Contextual component 112 may be configured to obtain contextualinformation describing events occurring at or near user geographiclocation. Contextual information may include information about eventsthat have previously occurred at or near user geographic location.Contextual information may include information about events that occuron an individual basis. A one-time political demonstration occurring ata certain geographic location may be one such example of an eventoccurring on an individual basis. Contextual information may includeinformation about events that occur periodically, regularly, and/orsporadically. For example, a baseball game that takes place every otherTuesday during the months of April through October is an event occurringperiodically, a commuter train that arriving every morning at 8:30 is aregularly occurring event, and a unscheduled road hazard repair is asporadically occurring event. Contextual information may vary in itsspecificity and its semantic content based on the user demographicinformation, user behavior information, and geographic location. Forexample, contextual information regarding a bachelor party celebrationat a certain bar may be relevant to a user that is currently located inthe bar and who in the past was arrested for an assault during a baraltercation. Conversely, a bachelor party celebration at a certain barmay not be relevant to a user that is attending a symphony performanceduring the same time at a nearby symphony hall. Contextual component 112may be configured to obtain contextual information from a variety ofsources. Contextual component 112 may be configured to obtain contextualinformation as stated information from the user, from an administrator,obtained from analysis of publicly available information associated withthe geographic location (e.g., public records, social media, and/orother sources), determined based on behavioral and/or demographicinformation related to the user, and/or other sources of contextualinformation. For example, contextual information may be obtained fromlocal, national, and international news aggregation engines, local andmunicipal agencies, mass-transit schedules, motor vehicle traffic data,local, national, and international weather reports, natural hazardsprograms, online public content and/or other sources. An online publiccontent may include one or more of an online video content, a socialmedia content, an online photo content, audio content, and/or otheronline public content. An online platform may include a networkingplatform a media platform, and/or other online platforms. The onlineplatform may include the online public content and/or make the onlinepublic content available for consumption. For example, an onlineplatform may include YOUTUBE, FACEBOOK, TWITTER, PINTEREST, LINKEDIN,FOURSQUARE, GOOGLE+, FLICKR, TUMBLR, BLOGGER, VINE, INSTAGRAM, SNAPCHAT,MAKER.TV and/or other online platforms. In some implementations,contextual component 112 may be configured to obtain contextualinformation from proprietary data sets.

Determination component 114 may be configured to determine a likelihoodof incident occurrence for a user at a certain geographic location byanalyzing information obtained by geolocation component 106, usercomponent 108, incident component 110, and contextual component 112.Information obtained by geolocation component 106 may include time ofdetermination, user's geographic location, speed of travel, and otherinformation. Information obtained by user component 108 may includeuser's demographic characteristics including user behavioralinformation, user social information, and other information pertainingto the user. User demographic characteristics may include user's age,sex, marital status, height and weight, occupation, and other suchcharacteristics. User behavioral information may include informationregarding user activities at the geographic location (e.g., celebratingfriend's birthday at a restaurant). User social information may includedata related to user's profile stored in connection with user's accountwith a social networking system such as Facebook. User socialinformation may include information related to user hobbies andpreferences, recent public posts, interactions with other members withinthe social networking system. Information obtained by incident component110 may include information related to historical incidents that havepreviously occurred at or near user's geographic location. Historicalincident information may include historical incidents that previouslyoccurred at or near user's geographic location and may have had certaineffects on certain population group therein. Historical incidentinformation may include crime statistics, health and safety information,traffic information, and/or other information obtained based on userinformation. Information obtained by contextual component 112 mayinclude information about events that are occurring, previouslyoccurred, or will occur within a relevant time frame, at or near user'scurrent geographic location obtained based on user information. Forexample, in the determination analysis shown in FIG. 2, the analysis mayinclude a Bayesian-type statistical analysis 220 performed at time 201on user geographic location information 202 obtained from user's phone,user generated demographic information 203, user social data 204including data from user accounts on Facebook and Twitter, user'sschedule and other user behavioral information 204, relevant historicalincident data 206, and contextual information 207. Time 201 may be 8:30PM. Geographic location 202 may be a 3-mile radius around a bar locatedin the Lower Manhattan neighborhood of New York City. User demographiccharacteristics 203 may include a 23-year-old, Caucasian, female, whorecently became unemployed. User social information 204 may includevideo and photo information that may be used to determine that useroften engages in late night bar hopping and is newly single. Userbehavioral information 205 may include information that user is within ablock from her house. Historical incident information 206 may includeinformation from both law enforcement sources and social networkingsites relating to high incidents of rape reported at the bar that useris currently located at. Contextual information 207 may includeinformation that most of rape incidents reported were reported occurringafter 2 AM. Determination analysis 220 may determine likelihood ofincident occurrence 208 to be 25 in 100,000 at time 201 at geographiclocation 202 for user having user demographic characteristics 203, usersocial information 204, user behavioral information, historical incidentinformation 206, and contextual information 207.

Referring back to FIG. 1, the determination analysis performed bydetermination component 114 may be configured to utilize a variety ofanalytical techniques to analyze collected sets of user data, historicalincident data, and contextual information obtained from various sourcesto create a single incidence likelihood indicator. Determinationcomponent 114 may be configured to determine a likelihood of incidentoccurrence using Bayesian-type statistical analysis to calculate theincidence likelihood indicator. Determination component 114 may beconfigured to assign specificity, relevance, confidence and/or weight toevery one of geographic information, user information, historicalincident information, and contextual information based on the relevanceand relationship between each piece of information to one another. Theassignment of these weight factors may be used in determination ofuser-specific likelihood results. For example, during a likelihooddetermination a higher weight may be given to an increased incidence ofassaults of young females in neighborhood bars when a user is a youngfemale planning on visiting one of the neighborhood bars as when a useris a young male planning on visiting one of the neighborhood bars.

In some implementations, incomplete data sets may be obtained. Bayesiannetwork model may provide statistical validity to determination analysisand hierarchically apply relevant data in ‘best available’ model. Forexample, access to national level homicide and violent crimevictimization rates only may be available in Guatemala. No demographicinformation about the victims or details about the geolocation of crimesmay be available. A subsequent study published by a local academicinstitution may provide additional victimization data from the analysisof crimes limited to the urban areas of Guatemala City. The study mayinclude more current data, demographic information about victims, andspecific geolocation information within a city-block level accuracyabout the incidents. This additional information may be used to compoundand/or supersede the currently available crime data, and may be used todetermine a likelihood of incidence for certain individuals within thegeographical location of Guatemala City and/or other locations. In someimplementations, this additional information may be used to compoundand/or supersede the currently available crime data, and may be used todetermine a likelihood of incidence including a type of incident, forcertain individuals within the geographical location of Guatemala Cityand/or other locations.

In some implementations, determination component 114 may be configuredto assign specificity, relevance, confidence and/or weight to every oneof geographic information, user information, historical incidentinformation, and contextual information based on the source of theinformation. The selection of these weighting factors may be used toaugment the predictive power of the likelihood determination analysis.For example, more established and highly frequented social network sitesmay be associated with a higher credibility factor, while newer, lessestablished sites may be associated with a relatively lower credibilityfactor. A higher weight factor may be assigned to a post about anupcoming political rally by a well-known activist group using a popularsocial network site than a post by an individual with no history ofactivism or known affiliation with the group.

In some implementations, user information, historical incidentinformation, contextual information and/or other information may be usedin conjunction with one or more predictive models. The predictivemodel(s), in various implementation, may include one or more of neuralnetworks, Bayesian networks (such as Hidden Markov models), expertsystems, decision trees, collections of decision trees, support vectormachines, or other systems known in the art for addressing problems withlarge numbers of variables. The specific information analyzed may varydepending on the desired functionality of the particular predictivemodel.

Determination component 114 may be configured to determine alocation-based likelihood indicators associated with the individualusers based upon user information, historical incident information, andrelevant contextual information. The likelihood indicators may quantifya likelihood of negative incident occurrence to individual users at acertain geographic location (e.g., a bar, a city street). The likelihoodindicators may be determined based upon the relevant informationincluding one or more of demographic information, behavioralinformation, social information, biometric information, supplementalinformation, historical incident information, relevant contextualinformation, and/or any other information related to the user, user'sgeographic location, and the time of user presence at geographiclocation. Likelihood indicators may be a sliding scale of percentilevalues (e.g., 10%, 15%, . . . n, where a percentage may reflectlikelihood of incident occurrence), numerical values (e.g., 1, 2, . . .n, where a number may be assigned as low and/or high), verbal levels(e.g., very low, low, medium, high, very high, and/or other verballevels), and/or any other scheme to represent a confidence score.Individual likelihood indicators may have one or more likelihoodindicators associated with it. An aggregate likelihood indicator for auser at a large geographic location may represent a likelihood ofincident occurrence over multiple smaller geographic locationscomprising the larger area. The aggregate likelihood indicator may bedetermined based on a combination of likelihood indicators associatedwith the individual locations and the information associated with theuser at each location, and/or other basis.

In some implementations, the likelihood indicators may quantify alikelihood of occurrence of certain types of negative incidents toindividual users at a certain geographic location. For example, thelikelihood indicators may quantify a likelihood of certain negativeincidents (e.g., assault, battery, theft, etc.) occurrence to individualusers at a certain geographic location (e.g., a bar, a city street).

Determination component 114 may be configured to iteratively update alikelihood indicator for individual user at a certain geographiclocation (e.g., periodically, based on recurring triggering event(s),and/or at other intervals). System 100 may be configured to continuedetermining and/or obtaining user information, historical incidentinformation, and contextual information utilized by determinationcomponent 114 to re-analyze and determine updated likelihood of incidentoccurrence based on updated user information, historical incidentinformation, contextual information, and/or other information within acertain time interval. For example, a user may receive a likelihoodindicator of 25% at a certain location based on the information analyzedby System 100 at a first time period. The same user in the same locationmay receive a likelihood indicator of 35% based on the informationanalyzed by System 100 at a second time period. The change in likelihoodof incident occurrence may be related to a change in user information,historical incident information, contextual information, and/or otherinformation utilized by determination component 114 in determininglikelihood indicator.

In some implementations, determination component 114 may be configuredto iteratively update a likelihood indicator based on a change in user'sgeographic location. For example, a user may be traveling from onedestination to the next. System 100 may be configured to continuedetermining and/or obtaining user information, historical incidentinformation, and contextual information utilized by determinationcomponent 114 to re-analyze and determine updated likelihood of incidentoccurrence based on updated user information, historical incidentinformation, contextual information, and/or other information at eachgeographic location the user is present. For example, a user may receivea likelihood indicator of 25% at a first location based on theinformation analyzed by system 100. The same user in a second locationmay receive a likelihood indicator of 15% based on the informationanalyzed by System 100. The change in likelihood of incident occurrencemay be related to a change in user information, historical incidentinformation, contextual information, and/or other information utilizedby determination component 114 in determining likelihood indicator at asecond location.

Presentation component 116 may be configured to effectuate presentationof an incidence likelihood indicator to the user, the incidencelikelihood indicator reflecting the likelihood of incident occurrencefor the geolocation and the user. Presentation component 116 may beconfigured to use client computing device(s) 104 to present theincidence likelihood indicator to the user. In some implementations,client computing device(s) 104 may include one or more of a smartphone,a tablet, a mobile device, and/or other displays. A given clientcomputing device 104 may include one or more processors configured toexecute computer program components. The computer program components maybe configured to enable a user associated with the given clientcomputing device 104 to interface with system 100 and/or externalresources 120, and/or provide other functionality attributed herein toclient computing device(s) 104.

Referring to FIG. 3, four users, 1-4, at geographic location 300 at time301 are shown. System 100 via geolocation component 106 of FIG. 1, maydetermine and/or obtain geolocation information relating to geographiclocation 300 including a New York City block located on 42^(nd) Streetbetween 7^(th) and 8^(th) Avenue. System 100 via user component 108 ofFIG. 1, may determine and/or obtain user information relating to each ofthe four users. User information obtained for individual user mayinclude user demographic information, user behavioral information, usersocial information, user biometric information. User informationobtained for user 1 including that user 1 is an 18 year oldAfrican-American male, 6′2 tall, weighing 210 lbs., unemployed, a memberof the “Black Lives Matter” social media group, has a YouTube channel towhich he posts amateur rap videos, his contact on Facebook posted a“wall post” about stop & frisk policy, his heart rate is elevated ascompared to his baseline heart rate, and he is currently walking southon 42^(nd) Street. User information obtained for user 2 including thatuser 2 is a 25-year-old Caucasian female, 5′6″ 125 lbs., blonde, blueeyed, employed as a bartender, enrolled in a creative writing course, isscheduled to work later tonight, has recently entered a “Fitness & Diet”group on Facebook, and she is currently walking west on 7^(th) Avenue.User information obtained for user 3 including that user 3 is a38-year-old African-American male, working as an obstetrician at a localhospital, married, has 2 kids, is a car racing enthusiast, and iscurrently driving south on 42^(nd) Street. User information obtained foruser 4 including that user 4 is a 35-year-old female, married, hasannounced that she is expecting via her Twitter account, has recentlyreturned from a volunteer mission in Cameroon, tends to do most of hershopping online, and he is currently walking south on 42^(nd) Street.

System 100 via incident component 110 of FIG. 1, may determine and/orobtain historical incident event information 302 relating to incidentsthat have previously occurred at or near geographic location 300.Historical incident information 302 obtained for geographic location 300may include reports by national law enforcement agency of higher thannational average rates of auto theft, robbery, larceny, identity theft,rape, and food related poisoning. System 100 via contextual component112 of FIG., may determine and/or obtain contextual information 303relating to events occurring at or near geographic location 300.Contextual information 303 may include information of regular policepatrol at or near time 301 at or near geolocation 300, an eyewitnessreport on social media of a convenience store robbery at gunpoint by anAfrican-American male at or near geolocation 300 within the last hour, alocal news interview of 5 owners who had their luxury sports cars stolenat or near geolocation 300 within last week, Facebook posts by usersthat checked into a coffee shop at geolocation 310 and have reported 3incidents of identity theft and 2 incidents of stolen laptop computerwithin the last 3 months, local news interviews with 3 rape victims thatwere assaulted at or near geolocation 300, 2 of which were blond andblue eyed, a scheduled arrival of a flight from Senegal using a PortAuthority stop for an airport bus located at or near geolocation 300, aNew York Sanitation report that found disease causing bacteria in anapple pie sold by many local cafes, a Latin-American business conventionthat started the day before time 301 with some events taking place at ornear geolocation 300, a church organized anti-abortion picket at or neargeolocation 300, and reports of high winds and rain within 30 minutes oftime 301 at geolocation 300. System 100 may rate historical incidentinformation 302 and contextual information 303 in terms ofapplicability, significance, and weight based on individual userinformation to determine a highly individualized incidence occurrenceindicator.

At first pass, historical incident information of higher than nationalaverage crime and health and safety statistics may imply a highlikelihood of incidence to all the users. System 100 may determine thatat geolocation 300 at time 301, not all the users will have the samelikelihood of incident occurrence. User 1 may have a 90% likelihood ofincident occurrence because of the historical incident events andcontextual information evaluated within the framework of user 1characteristics. For example, higher weight may be assigned to suchinformation as recent robbery by African-American male, whose physicaldescription closely matches that of user 1, user 1's support ofanti-police rhetoric, elevated heart rate after reading a negativecomment may indicate he may resist potential police questioning. User 2may have a 65% likelihood of incident occurrence because of thehistorical incident events and contextual information evaluated withinthe framework of user 2 characteristics. For example, higher weight maybe assigned to such information as recent reports of rape of similarlylooking victims as user 2, reports of stolen laptops in coffee shops,given the fact that user 2 will likely be using her laptop to finish hercreative writing assignment in a coffee shop to which she is most likelywalking to as she is not near her home or work. User 3 may have a 45%likelihood of incident occurrence because of the historical incidentevents and contextual information evaluated within the framework of user3 characteristics. For example, higher weight may be assigned to suchinformation as reports of stolen luxury sports cars, given the fact thatuser 3 is currently driving a luxury sports car, an influx of touristsfrom Senegal that may potentially carry viruses like Malaria for whichuser 3 has not been vaccinated, an encounter with an anti-abortionpicket given that user 3 has likely performed birth terminatingprocedures during his practice. User 4 may have a 25% likelihood ofincident occurrence because of the historical incident events andcontextual information evaluated within the framework of user 4characteristics. For example, higher weight may be assigned to suchinformation as a near buy convention of business people from LatinAmerica given that they may potentially carry a virus that is known tocause birth defects, incidents of food poisoning related to ingestion ofapple pie given that bad weather may likely force user 4 into a near-bycoffee shop where pregnant user 4 may potentially order a slice of applepie, and reports of identity theft given that user 4 tends to pay with acredit card for all her purchases. These examples are not meant to be alimitation of this disclosure, as likelihood of incident occurrence maybe associated with information resulting in a higher and/or lowerlikelihood of incident occurrence based upon various other factors.

Referring back to FIG. 1, determining the likelihood of incidentoccurrence for a user may be based upon a future destination a userintends to visit. A future destination may include a geographic locationthat user inputs into System 100. In some implementations, geolocationcomponent 106 may be configured to obtain user geographic location fromthe user's calendar. A user's calendar may include one or more of acalendar associated with client computing device 104, a publiclyavailable calendar associated with user's social media profile, aprivate calendar associated with a third-party application that storesuser's schedule data, and/or other calendars. A future destination mayinclude a geographic location determined and/or obtained by geolocationcomponent 106 using a geographic location associated with an entry onuser's calendar. Incident component 110 may be configured to obtainhistorical incident information for incidents that have previouslyoccurred at or near user's future geographic location. Incidentcomponent 110 may be configured to obtain historical incidentinformation from a variety of sources. User component 108 may beconfigured to obtain user demographic characteristics. User component108 may be configured to obtain user behavioral information at a futuregeographic location. User component 108 may be configured to obtain usersocial information. User component 108 may be configured to obtain usersupplemental information. User component 108 may be configured to obtainuser biometric information. User component 108 may be configured toobtain information about other users and/or animals that may accompanyuser at future geographic location. Contextual component 112 may beconfigured to obtain contextual information describing events occurringat or near future user geographic location. Determination component 114may be configured to determine a likelihood of incident occurrence for auser at a future location by analyzing information obtained bygeolocation component 106, user component 108, incident component 110,contextual component 112.

System 100 may be configured to track likelihood of incident occurrenceat a particular geographic location. Tracking the likelihood of incidentmay include one or more of tracking likelihood of incident occurrencefor all users as an aggregate indicator, tracking likelihood of incidentoccurrence for all users of specific demographic population, trackingthe accuracy of the determination of likelihood of incident occurrencefor all users as an aggregate indicator, tracking the accuracy of thedetermination of likelihood of incident occurrence for all users ofspecific demographic population, tracking user responses to an incidentoccurrence for all users as an aggregate indicator, tracking userresponses to an incident occurrence for all users of specificdemographic, tracking whether users that have experienced an adverseincident determined by System 100 subsequently return to the geographiclocation at which likelihood of incident occurrence is substantial.

System 100 may be configured to track likelihood of incident occurrenceat a particular geographic location for a particular user. Tracking thelikelihood of incident occurrence may include one or more of trackinglikelihood of incident occurrence, tracking the accuracy of thedetermination of likelihood of incident occurrence, tracking userresponse to an incident occurrence, tracking whether user that hasexperienced an adverse incident determined by System 100 subsequentlyreturns to the geographic location at which likelihood of incidentoccurrence is substantial.

Presentation component 116 may be configured to effectuate presentationof an incidence likelihood indicator related to a certain user at acertain geographic location to additional users. Presentation component116 may be configured to effectuate presentation of an incidencelikelihood indicator based on a change in a likelihood of incidencerelated to a certain user at a certain geographic location to additionalusers. In some implementations, system 100 may alert law enforcementand/or other users if the likelihood of incident occurrence for a useror a population of users at a certain geographic location exceeds apredetermined threshold value. For example, a parent may receive analert that their child's activities result in a 10% increase in alikelihood of incidence.

Referring again to FIG. 1, in some implementations, server(s) 102,client computing platform(s) 104, and/or external resources 120 may beoperatively linked via one or more electronic communication links. Forexample, such electronic communication links may be established, atleast in part, via network(s) 119 such as the Internet and/or othernetworks. The network(s) 119 may comprise one or both of wired orwireless communications. It will be appreciated that this is notintended to be limiting, and that the scope of this disclosure includesimplementations in which server(s) 102, client computing platform(s)104, and/or external resources 120 may be operatively linked via someother communication media.

A given client computing platform 104 may include one or more processorsconfigured to execute computer program components. The computer programcomponents may be configured to enable a producer and/or user associatedwith the given client computing platform 104 to interface with system100 and/or external resources 120, and/or provide other functionalityattributed herein to client computing platform(s) 104. By way ofnon-limiting example, the given client computing platform 104 mayinclude one or more of a desktop computer, a laptop computer, a handheldcomputer, a NetBook, a Smartphone, a gaming console, and/or othercomputing platforms.

External resources 120 may include sources of information, hosts and/orproviders of virtual environments outside of system 100, externalentities participating with system 100, and/or other resources. In someimplementations, some or all of the functionality attributed herein toexternal resources 120 may be provided by resources included in system100.

Server(s) 102 may include electronic storage 122, one or more processors124, and/or other components. Server(s) 102 may include communicationlines, or ports to enable the exchange of information with a networkand/or other computing platforms. Illustration of server(s) 102 in FIG.1 is not intended to be limiting. Servers(s) 102 may include a pluralityof hardware, software, and/or firmware components operating together toprovide the functionality attributed herein to server(s) 102. Forexample, server(s) 102 may be implemented by a cloud of computingplatforms operating together as server(s) 102.

Electronic storage 122 may include electronic storage media thatelectronically stores information. The electronic storage media ofelectronic storage 122 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server(s)102 and/or removable storage that is removably connectable to server(s)102 via, for example, a port (e.g., a USB port, a firewire port, etc.)or a drive (e.g., a disk drive, etc.). Electronic storage 122 mayinclude one or more of optically readable storage media (e.g., opticaldisks, etc.), magnetically readable storage media (e.g., magnetic tape,magnetic hard drive, floppy drive, etc.), electrical charge-basedstorage media (e.g., EEPROM, RAM, etc.), solid-state storage media(e.g., flash drive, etc.), and/or other electronically readable storagemedia. The electronic storage 122 may include one or more virtualstorage resources (e.g., cloud storage, a virtual private network,and/or other virtual storage resources). Electronic storage 122 maystore software algorithms, information determined by processor(s) 124,information received from server(s) 102, information received fromclient computing platform(s) 104, and/or other information that enablesserver(s) 102 to function as described herein.

Processor(s) 124 may be configured to provide information processingcapabilities in server(s) 102. As such, processor(s) 124 may include oneor more of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor(s) 124 is shown in FIG. 1 asa single entity, this is for illustrative purposes only. In someimplementations, processor(s) 124 may include a plurality of processingunits. These processing units may be physically located within the samedevice, or processor(s) 124 may represent processing functionality of aplurality of devices operating in coordination. The processor(s) 124 maybe configured to execute computer readable instruction components 106,108, 110, 112, 114, and/or other components. The processor(s) 124 may beconfigured to execute components 106, 108, 110, 112, 114, and/or othercomponents by software; hardware; firmware; some combination ofsoftware, hardware, and/or firmware; and/or other mechanisms forconfiguring processing capabilities on processor(s) 124.

It should be appreciated that although components 106, 108, 110, 112,114 and 116 are illustrated in FIG. 1 as being co-located within asingle processing unit, in implementations in which processor(s) 124includes multiple processing units, one or more of components 106, 108,110, 112, 114 and 116 may be located remotely from the other components.The description of the functionality provided by the differentcomponents 106, 108, 110, 112, 114 and/or 116 described herein is forillustrative purposes, and is not intended to be limiting, as any ofcomponents 106, 108, 110, 112, 114 and/or 116 may provide more or lessfunctionality than is described. For example, one or more of components106, 108, 110, 112, 114 and/or 116 may be eliminated, and some or all ofits functionality may be provided by other ones of components 106, 108,110, 112, 114 and/or 116. As another example, processor(s) 124 may beconfigured to execute one or more additional components that may performsome or all of the functionality attributed herein to one of components106, 108, 110, 112, 114 and/or 116.

FIG. 4 illustrates a method 400 for determining a likelihood of incidentoccurrence for a user at a certain geographic location, in accordancewith one or more implementations. The operations of method 400 presentedbelow are intended to be illustrative. In some implementations, method400 may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of method 400 areillustrated in FIG. 4 and described below is not intended to belimiting.

In some implementations, method 400 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 400 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 400.

At an operation 402, geographic information of the incident occurrencedetermination may be obtained. Geographic information may includeinformation describing the user location. Operation 402 may be performedby a geolocation component that is the same as or similar to contentcomponent 106, in accordance with one or more implementations.

At an operation 404, user information associated with the user presentat geographic location may be obtained. User information may includeuser demographic information, user behavioral information, user socialinformation, user supplemental information, user biometric information,and/or other information. Operation 404 may be performed by a usercomponent that is the same as or similar to user component 108, inaccordance with one or more implementations.

At an operation 406, historical incident information associated with thegeographic location may be obtained. Historical incident information mayinclude data relating to crime, mortality, injury, morbidity rates,and/or other information. Operation 406 may be performed by an incidentcomponent that is the same as or similar to incident component 110, inaccordance with one or more implementations.

At an operation 408, contextual information associated with thegeographic location may be obtained. Operation 408 may be performed by acontextual component that is the same as or similar to contextualcomponent 112, in accordance with one or more implementations.

At an operation 410 a likelihood of incident occurrence for a user atgeographic location may be determined. The likelihood of incidentoccurrence may quantify a likelihood of a negative incident occurrenceto user at a certain geographic location. The determination may be basedupon the geographic location, user information, historical incidentinformation, and/or contextual information. Operation 410 may beperformed by a determination component that is the same as or similar todetermination component 114, in accordance with one or moreimplementations.

At an operation 412 a likelihood of incident occurrence indicator may bepresented to the user. Operation 412 may be performed by a presentationcomponent that is the same as or similar to presentation component 116,in accordance with one or more implementations.

Although the system(s) and/or method(s) of this disclosure have beendescribed in detail for the purpose of illustration based on what iscurrently considered to be the most practical and preferredimplementations, it is to be understood that such detail is solely forthat purpose and that the disclosure is not limited to the disclosedimplementations, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present disclosure contemplates that, to the extent possible, one ormore features of any implementation can be combined with one or morefeatures of any other implementation.

What is claimed is:
 1. A system for providing access to incidentinformation, the system comprising: one or more physical processorsconfigured by machine-readable instructions to: obtain user information,the user information including demographic information of a user,geolocation information of the user, user behavioral information, andsocial information, of the user, such that the demographic informationindicating demographic characteristics of the user including occupationof the user, the geolocation information indicating geolocations of theuser over time, the user behavioral information indicating at least oneactivity being performed by the user at or near the geolocations of theuser, and the social information including a user profile of the userrelated to a social networking platform and indicating frequency ofactivities of the user, wherein the geolocations are based on usermovement of the user to different geolocations; obtain historicalincident information for incidents that have previously occurred at ornear the geolocations as the user moves to the geolocations; determine,in an ongoing manner, a likelihood of occurrence of an incident thatnegatively impacts the safety and/or security of the user for individualones of the geolocations and the user based upon the user demographiccharacteristics, the user behavioral information, the socialinformation, and the historical incident information as the geolocationschange based on the user movement to the individual geolocations; andeffectuate presentation of an incidence likelihood indicator to theuser, the incidence likelihood indicator reflecting the likelihood ofoccurrence of an incident that negatively impacts the safety and/orsecurity of the user for the geolocations and the user, wherein theincidence likelihood indicator is a value on an ordered scale.
 2. Thesystem of claim 1, wherein the determination of the likelihood ofoccurrence of an incident that negatively impacts the safety and/orsecurity of the user is determined using predictive models.
 3. Thesystem of claim 1, wherein the one or more physical processors arefurther configured to obtain a query time at which the user is or willbe present at the geolocation based on calendar events of the user andwherein the determination of the likelihood of occurrence of an incidentthat negatively impacts the safety and/or security of the user for thegeolocation is further configured to be based upon the query time. 4.The system of claim 3, wherein the one or more physical processors arefurther configured to obtain the calendar events based on the userbehavioral information.
 5. The system of claim 3, wherein the one ormore physical processors are further configured to obtain contextualinformation for the geolocation, the contextual information describingevents occurring at or near the geolocation at the query time.
 6. Thesystem of claim 5, wherein the events have previously occurredindividually, periodically, regularly, and/or sporadically.
 7. Thesystem of claim 5, wherein the contextual information includes eventsrelated to natural or man-made hazards and disasters, states ofemergency, political unrest, social unrest, and/or wars.
 8. The systemof claim 5, wherein the determination of the likelihood of occurrence ofan incident that negatively impacts the safety and/or security of theuser for the geolocation and the user is based upon the contextualinformation.
 9. The system of claim 1, wherein the demographiccharacteristics include at least one or more of the user's age, gender,and/or race.
 10. The system of claim 1, wherein the historical incidentinformation includes at least one or more of a crime information, avictimization information, a health and safety information, and/or aclimate information.
 11. A method for providing access to incidentinformation, the method comprising: obtaining user information, the userinformation including user demographic information, user geolocationinformation, user behavioral information, and social information suchthat the user demographic information indicating demographiccharacteristics of the user including occupation of the user, the usergeolocation information indicating geolocations of the user over time,the user behavioral information indicating at least one activity beingperformed by the user at or near the geolocations of the user, and thesocial information including a user profile of the user related to asocial networking platform and indicating frequency of activities of theuser, wherein the geolocations are based on user movement of the user todifferent geolocations; obtaining historical incident information forincidents that have previously occurred at or near the geolocations asthe user moves to the geolocations; determining, in an ongoing manner, alikelihood of occurrence of an incident that negatively impacts thesafety and/or security of the user for individual ones of thegeolocation and the user based upon the user demographiccharacteristics, the user behavioral information, the socialinformation, and the historical incident information as the geolocationschange based on the user movement to the individual geolocations; andeffectuating presentation of an incidence likelihood indicator to theuser, the incidence likelihood indicator reflecting the likelihood ofoccurrence of the incident that negatively impacts the safety and/orsecurity of the user for the geolocations and the user, wherein theincidence likelihood indicator is a value on an ordered scale.
 12. Themethod of claim 11, wherein the determination of the likelihood ofoccurrence of the incident that negatively impacts the safety and/orsecurity of the user is determined using predictive models.
 13. Themethod of claim 11, wherein the one or more physical processors arefurther configured to obtain a query time at which the user is or willbe present at the geolocation based on calendar events of the user andwherein the determination of the likelihood of occurrence of theincident that negatively impacts the safety and/or security of the userfor the geolocation is further configured to be based upon the querytime.
 14. The method of claim 13, wherein the one or more physicalprocessors are further configured to obtain the calendar events based onthe user behavioral information.
 15. The method of claim 13, wherein theone or more physical processors are further configured to obtaincontextual information for the geolocation, the contextual informationdescribing events occurring at or near the geolocation at the querytime.
 16. The method of claim 15, wherein the events have previouslyoccurred individually, periodically, regularly, and/or sporadically. 17.The method of claim 15, wherein the contextual information includesevents related to natural or man-made hazards and disasters, states ofemergency, political unrest, social unrest, and/or wars.
 18. The methodof claim 15, wherein the determination of the likelihood of occurrenceof the incident that negatively impacts the safety and/or security ofthe user for the geolocation and the user is based upon the contextualinformation.
 19. The method of claim 11, wherein the demographiccharacteristics include at least one or more of the user's age, gender,and/or race.
 20. The method of claim 11, wherein the historical incidentinformation includes at least one or more of a crime information, avictimization information, a health and safety information, and/or aclimate information.